Assessment of a human computer interface prototyping environment

A Human Computer Interface (HCI) prototyping environment with embedded evaluation capability has been successfully assessed which will be valuable in developing and refining HCI standards and evaluating program/project interface development, especially Space Station Freedom on-board displays for payload operations. The HCI prototyping environment is designed to include four components: (1) a HCI format development tool, (2) a test and evaluation simulator development tool, (3) a dynamic, interactive interface between the HCI prototype and simulator, and (4) an embedded evaluation capability to evaluate the adequacy of an HCI based on a user's performance

Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research

Field of vision (A-pillar geometry) - a review of the needs of drivers: final report.

It is the responsibility of the Department of the Environment, Transport and the Regions (DETR) to improve the safety of the UK road network. Driver vision has been identified as a significant factor and the possible causes of reduced vision require further investigation. This study was commissioned to identify the problems associated with restricted visibility for vehicle drivers, in particular the consequential risk to road safety of changes in A-pillar size and position, driven by the need for improved structural and aerodynamic performance, which can restrict driver vision. The experimental trials supported the findings of the literature review and the driver survey that A-pillars do impede the driver’s forward field of view. It was shown that: • Approximately one third of all the targets presented in the vicinity of the A-pillar were not detected. • A-pillar obscuration is a greater problem in newer, as opposed to older, cars (although this is only statistically significant for viewing past the off-side A-pillar). • If drivers make the effort to ‘look around’ their A-pillars the visual problems caused by A-pillar obscuration can be significantly reduced. However such a strategy cannot be relied upon and may be unsafe to encourage if the driver should also be concentrating on the road ahead. The study therefore shows that there are safety disbenefits due to the trend for wider A-pillars. Further research work into A-pillar design should be considered in terms of the drivers’ field of view which may include: • the use of accident statistics to quantify the effect of A-pillar design on driver vision, • a study to investigate the relationship between target detection and obscuration angle (ranging from 0° to 6°), this may also include an investigation into the location of the A-pillar in the drivers’ visual field, • creating awareness amongst interested parties of the visual effects of increased A-pillar thickness

Quality and field of vision - a review of the needs of drivers and riders: phase 1 report.

It is the responsibility of the Department of the Environment, Transport and the Regions (DETR) to promote safe use of the UK road network. Driver vision has been identified as major determinant to this and factors which impede it require further investigation. This project is specifically concerned with the effect on driver / rider vision of: • windscreen and visor tinting, installed light transmission, haze, abrasion, damage and repair, • the use and positioning of wider structural member (particularly A-pillars) to improve crashworthiness, aerodynamics and rigidity. The first phase of the work is to understand the current status regarding the above factors and the rationale for it. A variety of sources have been consulted for this information including: • establishing contact with interested parties, • reviewing and establishing the basis of EC Directives and other regulations, • reviewing and summarising previous, current and proposed research, • analysing available accident data, • interviewing drivers and riders. A detailed work plan for the remainder of the project has been developed based on the results of the Phase 1 findings and on the professional experience ICE Ergonomics has in this field

The definition, production and validation of the direct vision standard (DVS) for HGVS. Final Report for TfL review

This report presents research performed by Loughborough Design School (LDS) on behalf of Transport for London. The research has been conducted against a background of over representation of heavy goods vehicles (HGVs) being involved in road traffic accidents with vulnerable road users (VRUs) where ‘failed to look properly’ and ‘vehicle blind-spot’ are often reported as the main casual factors in the accident data. Previous work by LDS on driver’s vision from HGVs has identified the need to reduce reliance on indirect vision via mirrors through the specification of a direct vision standard (DVS) for HGVs. Recent work commissioned by TfL and performed by the Transport Research Laboratory (TRL) resulted in a draft DVS. This draft DVS has been evaluated and reworked by the LDS team to produce a viable and robust method to quantify direct vision performance of an HGV together with a means to rate that vision performance against a star rating standard. Throughout this process significant stakeholder consultation has been used to support the development of the DVS. A total of 27 vehicles representing the majority of the current Euro 6 N3 HGV fleet have been modelled in CAD. Where data were available these have been mounted at the highest, lowest and most sold heights to produce a sample of 54 test vehicles. A methodology has been developed that utilises volumetric projection of the field of view of the driver via the windows in the cab. This projection is then intersected with an assessment volume. The result is a volumetric representation of the space around a HGV cab that the driver can see to the front, driver and passenger sides. The volume of this space can be calculated to provide a rating of direct vision performance. An iterative design process was followed that explored different specifications of the assessment zone around the cab, factoring in the collision data with VRUs and the use of weightings to prioritise what needs to be seen. Two weighting schemes were evaluated one prioritising the volumes vertically, recognising the importance of being able to see closer to the ground, and a second prioritising the volumes directionally to address the prevalence of accidents being greater to the front and passenger side when compared to the driver’s side. The final specification of the volumetric assessment consists of a single, unweighted zone around the cab, informed by the current coverage of mirrors specified in UNECE regulation 46. This was done to foster direct vision that aims to remove the reliance on mirrors and thus should focus on providing direct vision of the areas currently covered by mirrors. The vehicle sample was then evaluated for its performance using this assessment, providing a volumetric score for each vehicle. These volumetric scores were then quantified by correlating them with a VRU simulation. Thirteen 5th %ile Italian female VRUs were placed around the vehicle and moved laterally to a point at which their head and shoulders could be seen. This served to provide context for the volumetric results such that a particular volume could be equated to an average distance at which the small adult could be seen. Furthermore, the VRU simulations provided a means to translate the volumetric performance into star ratings. Four star rating specifications were produced following an absolute (based on risk/safety) and a relative (based on the performance of the current fleet) approach. For both absolute and relative two iterations were proposed: 1. the VRU simulation distances were used to establish a threshold value, 2. the median volumetric result was used to establish a threshold value. The final option taken forwards used the VRU simulation distances for a 5th %ile Italian female to define the 1 star boundary. Vehicles able to provide direct vision of the VRUs at an average of <2m to the front, <4.5m to the passenger side and <0.6m to the driver’s side achieved a star rating 1 star or above. All others achieved a rating of zero star. Star ratings from 1 to 5 star were sub divided equally. The final result consists of three main outcomes: The definition, production and validation of the direct vision standard (DVS) for HGVs December 2018 Transport for London 4 Loughborough Design School © 1. A robust, repeatable and validated method for the volumetric analysis of direct vision performance using a CAD based process 2. A process to map a volumetric score for a given vehicle onto the 5 star rating scale to produce a DVS rating for any vehicle. 3. Star ratings for the majority of the Euro 6 N3/N3G HGV fleet showing that of the 41 configurations analysed, two vehicles are rated 5 star, no vehicles are rated 4 star, five vehicles are able to achieve 3 star, three vehicles are able to achieve 2 star, and six vehicles are able to achieve 1 star, the remainder 25 vehicles were rated as zero star

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classi- fier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios

Hitting the Bullseye: The Influence of Technical Debt on the Accuracy of Effort Estimation in Agile Projects

As firms rapidly develop solutions in order to increase revenue and market share, software development decisions considered to be temporary shortcuts and/or compromises may be implemented. These shortcuts represent “technical debt,” a metaphor which succinctly describes a software solution that should be “paid in full” or remediated in the future. Software architects and developers intend to resolve the “debt” in future product releases, but practitioners recognize that the challenge of always innovating may indefinitely postpone this remediation effort. Further, the accumulation of technical debt may have long term impact on the product’s maintainability by the software development teams and, consequently, impact the effort estimate delivered to management for forecasting product delivery timelines and product revenue expectations. While there are multiple publications that have studied effort estimation in traditional and agile software development strategies, there is limited research which considers technical debt during the estimation effort. As a result, the purpose of this dissertation is to design and propose a research model intended to determine whether or not the consideration of technical debt during the effort estimation process will improve the accuracy of the effort estimate in an agile project

The development of improvements to drivers' direct and indirect vision from vehicles - phase 1

This research project concerning "The development of improvements to drivers' direct and indirect vision from vehicles" has been designed to be conducted in two phases: . Phase 1 whose aim is to scope the existing knowledge base in order to prioritise and direct activities within Phase 2; . Phase 2 whose aim is to investigate specific driver vision problems prioritised in Phase 1 and determine solutions to them. This report details the activities, findings and conclusions resulting from the Phase 1 tasks undertaken

Quality and field of vision - a review of the needs of drivers and riders: final report.

Quality and field of vision - a review of the needs of drivers and riders: final report

WEATHER IMPACT ON ROAD ACCIDENT SEVERITY IN MARYLAND

This study was conducted to analyze and quantify the impact of weather factors on road accident severity, based on Maryland accident data during 2007-2010. In order to find a better model fitted related variables, three candidate models multinomial logit (MNL), ordered probit logit (OP), and neural networks were chosen to examine in SAS. The results showed that the Multilayer Perceptron Model in neural networks performed the best and is the accident severity model of choice. During the model construction, eight factors related to weather condition were considered. They were: air temperature, average wind speed, total precipitation in the past 24 hours, visibility, slight, moderate, heavy precipitation and relative humidity. Based on the comparison criteria, we concluded that MNL regression is more interpretive than OP and Neural Networks models. All factors except visibility and heavy precipitation had significant impact on accident severity when considering the data from the entire Maryland highway system. Using MNL, a data subset with accident records only in a section of US route 50 was examined. After excluding the impact factors other than weather, a narrow significant variable set was obtained